The input point cloud is randomly downsampled to a size of 20,480. The Lidar intensity values are appended to the x-y-z coordinates for each point. Consequently, the size of the input data is \(4\times 20,480\) .
During the first sampling-grouping-PointNet operation, the FPS operation extracts \(M_1=128\)  nodes denoted as \(\mathfrak {P}^{(1)}\) . The grouping procedure is exactly the same as the point-to-node method described in SO-Net [1]}.
As shown in Fig. REF , our PointNet-like module, which produces the feature \(P^{(1)}\) , is a slight modification of the original PointNet [2]}. At the second sampling-grouping-PointNet operation, the FPS extracts \(M_2=64\)  nodes denoted as \(\mathfrak {P}^{(2)}\) . The grouping step is a \(k\) NN-based operation as described in PointNet++ [3]}. Each node in \(\mathfrak {P}^{(2)}\)  are connected to its 16 nearest neighbors in \(\mathfrak {P}^{(1)}\) . The feature \(P^{(2)}\)  for each node in \(\mathfrak {P}^{(2)}\)  is obtained by the PointNet-like module shown in Fig. REF . Finally, a global point cloud feature vector is obtained by feeding \(\mathfrak {P}^{(2)}\)  and \(P^{(2)}\)  into a PointNet module shown in Fig.  REF
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